import torch
import torch.nn as nn
from .common import *
import ipdb

from NAS import operations
from NAS import gen_upsample_layer

def skip(model_index,
         num_input_channels=2, 
         num_output_channels=3, 
         num_channels_down=[16, 32, 64, 128, 128], 
         num_channels_up=[16, 32, 64, 128, 128],  
         num_channels_skip=[4, 4, 4, 4, 4], 
         filter_size_down=3, 
         filter_size_up=3, 
         filter_skip_size=1,
         need_sigmoid=True, 
         need_bias=True, 
         pad='zero', 
         upsample_mode='nearest', 
         downsample_mode='stride', 
         act_fun='LeakyReLU', 
         need1x1_up=True):

    """Assembles encoder-decoder with skip connections.

    Arguments:
        act_fun: Either string 'LeakyReLU|Swish|ELU|none' or module (e.g. nn.ReLU)
        pad (string): zero|reflection (default: 'zero')
        upsample_mode (string): 'nearest|bilinear' (default: 'nearest')
        downsample_mode (string): 'stride|avg|max|lanczos2' (default: 'stride')

    """
    assert len(num_channels_down) == len(num_channels_up) == len(num_channels_skip)

    n_scales = len(num_channels_down) 

    if not (isinstance(upsample_mode, list) or isinstance(upsample_mode, tuple)) :
        upsample_mode = [upsample_mode] * n_scales

    if not (isinstance(downsample_mode, list)or isinstance(downsample_mode, tuple)):
        downsample_mode = [downsample_mode] * n_scales
    
    if not (isinstance(filter_size_down, list) or isinstance(filter_size_down, tuple)) :
        filter_size_down = [filter_size_down] * n_scales

    if not (isinstance(filter_size_up, list) or isinstance(filter_size_up, tuple)) :
        filter_size_up = [filter_size_up] * n_scales
    # ipdb.set_trace()

    last_scale = n_scales - 1 

    cur_depth = None
    # ipdb.set_trace()
    model = nn.Sequential()
    model_tmp = model

    input_depth = num_input_channels
    for i in range(len(num_channels_down)):

        deeper = nn.Sequential()
        skip = nn.Sequential()

        if num_channels_skip[i] != 0:
            model_tmp.add(Concat(1, skip, deeper))
        else:
            model_tmp.add(deeper)
        
        model_tmp.add(bn(num_channels_skip[i] + (num_channels_up[i + 1] if i < last_scale else num_channels_down[i])))

        if num_channels_skip[i] != 0:
            skip.add(conv(input_depth, num_channels_skip[i], filter_skip_size, bias=need_bias, pad=pad))
            skip.add(bn(num_channels_skip[i]))
            skip.add(act(act_fun))
            
        # skip.add(Concat(2, GenNoise(nums_noise[i]), skip_part))

        deeper.add(conv(input_depth, num_channels_down[i], filter_size_down[i], 2, bias=need_bias, pad=pad, downsample_mode=downsample_mode[i]))
        deeper.add(bn(num_channels_down[i]))
        deeper.add(act(act_fun))

        deeper.add(conv(num_channels_down[i], num_channels_down[i], filter_size_down[i], bias=need_bias, pad=pad))
        deeper.add(bn(num_channels_down[i]))
        deeper.add(act(act_fun))

        deeper_main = nn.Sequential()

        if i == len(num_channels_down) - 1:
            # The deepest
            k = num_channels_down[i]
        
        else:
            deeper.add(deeper_main)
            k = num_channels_up[i+1]
            
        C_in = num_channels_down[i]
        C_out = num_channels_down[i]

        deeper.add(
          gen_upsample_layer.gen_layer(
            C_in=C_in,                            
            C_out=C_out,
            model_index=model_index
          )
        )

        model_tmp.add(conv(num_channels_skip[i] + k, num_channels_up[i], filter_size_up[i], 1, bias=need_bias, pad=pad))
        model_tmp.add(bn(num_channels_up[i]))
        model_tmp.add(act(act_fun))

        if need1x1_up:
            model_tmp.add(conv(num_channels_up[i], num_channels_up[i], 1, bias=need_bias, pad=pad))
            model_tmp.add(bn(num_channels_up[i]))
            model_tmp.add(act(act_fun))

        input_depth = num_channels_down[i]
        model_tmp = deeper_main

    model.add(conv(num_channels_up[0], num_output_channels, 1, bias=need_bias, pad=pad))
    if need_sigmoid:
        model.add(nn.Sigmoid())

    return model
